A compact Mamba-2 model performs end-to-end byte-level network traffic classification without tokenization or pre-training and remains competitive with substantially larger pre-trained systems.
Netgpt: Generative pretrained transformer for network traffic
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 1
baseline 1
citation-polarity summary
verdicts
UNVERDICTED 5representative citing papers
Nepco offloads network foundation models to SmartNICs using localized byte-sequence modeling and a pattern-aware convolutional architecture to achieve competitive macro F1 scores with 328x lower end-to-end latency than prior foundation models.
UniAlign improves robustness of deep learning NTC models under distribution shifts via domain alignment fine-tuning and stable ensembling, yielding 2.51% accuracy and 2.71% F1 gains over standard training on three public datasets.
In-domain BERT pretraining yields better low-FPR detection of DNS exfiltration than random initialization, with larger gains when more labeled fine-tuning data is available.
A survey reviewing statistical and deep learning approaches to synthetic network traffic generation, with comparisons, an AI comparison tool, open challenges, and future directions.
citing papers explorer
-
MambaNetBurst: Direct Byte-level Network Traffic Classification without Tokenization or Pretraining
A compact Mamba-2 model performs end-to-end byte-level network traffic classification without tokenization or pre-training and remains competitive with substantially larger pre-trained systems.
-
Versatile yet Efficient Network Traffic Analysis: Offloading Network Foundation Model to SmartNIC
Nepco offloads network foundation models to SmartNICs using localized byte-sequence modeling and a pattern-aware convolutional architecture to achieve competitive macro F1 scores with 328x lower end-to-end latency than prior foundation models.
-
UniAlign: A Model-Agnostic Framework for Robust Network Traffic Classification under Distribution Shifts
UniAlign improves robustness of deep learning NTC models under distribution shifts via domain alignment fine-tuning and stable ensembling, yielding 2.51% accuracy and 2.71% F1 gains over standard training on three public datasets.
-
Improving DNS Exfiltration Detection via Transformer Pretraining
In-domain BERT pretraining yields better low-FPR detection of DNS exfiltration than random initialization, with larger gains when more labeled fine-tuning data is available.
-
A Comprehensive Survey on Network Traffic Synthesis: From Statistical Models to Deep Learning
A survey reviewing statistical and deep learning approaches to synthetic network traffic generation, with comparisons, an AI comparison tool, open challenges, and future directions.